Digital cameras, whether embedded in other devices, or standalone cameras, rely heavily on automatic detection of where to focus in order to be user-friendly for the widest customer base possible. Autofocus and other automatic functions within a camera rely on automatic detection of what a user is likely to be interested in.
Current solutions can place too much weight on one factor or another in order to determine what portion of a given image frame is appropriate to focus on. This is sometimes called detection of salient objects within images. While focusing on one aspect of objects within an image frame, it is easy for a camera or image processing software to overlook other aspects of the image which may be just as interesting to a user, if not more interesting. One cannot simply take disparate approaches and stick them together, especially when the approaches have different methodologies.
Thus, a need still remains for a better saliency detection method. In view of the rapid increase in picture and video taking and sharing, it is increasingly critical that answers be found to these problems. In view of the ever-increasing commercial competitive pressures, along with growing consumer expectations and the diminishing opportunities for meaningful product differentiation in the marketplace, it is critical that answers be found for these problems. Additionally, the need to reduce costs, improve efficiencies and performance, and meet competitive pressures adds an even greater urgency to the critical necessity for finding answers to these problems.
Solutions to these problems have been long sought but prior developments have not taught or suggested any solutions and, thus, solutions to these problems have long eluded those skilled in the art.